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利用生物信息学分析鉴定滑膜肉瘤中与转移相关的生物标志物

Identification of Metastasis-Associated Biomarkers in Synovial Sarcoma Using Bioinformatics Analysis.

作者信息

Song Yan, Liu Xiaoli, Wang Fang, Wang Xiaoying, Cheng Guanghui, Peng Changliang

机构信息

Department of Nephrology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.

Department of Hematology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.

出版信息

Front Genet. 2020 Sep 11;11:530892. doi: 10.3389/fgene.2020.530892. eCollection 2020.

Abstract

Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (, , , , , , , , , and ) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.

摘要

滑膜肉瘤(SS)是一种具有高度侵袭性的软组织肿瘤,局部复发和转移风险高。然而,SS转移的潜在机制仍不清楚。本研究的目的是通过综合生物信息学分析筛选SS中与转移相关的生物标志物。选择两个mRNA数据集(GSE40018和GSE40021)来分析差异表达基因(DEGs)。使用注释、可视化和综合发现数据库(DAVID)和基因集富集分析(GSEA)对DEGs进行功能和通路富集分析。然后,通过相互作用基因检索工具(STRING)数据库构建蛋白质-蛋白质相互作用(PPI)网络。使用Cytoscape软件对PPI网络进行模块分析和枢纽基因验证。使用基于网络的基因集分析工具包(WebGestalt)对枢纽基因进行基因本体(GO)和京都基因与基因组百科全书(KEGG)通路分析。通过基因表达谱交互式分析(GEPIA)和Kaplan-Meier绘图仪数据库进一步评估枢纽基因的表达水平和生存分析。总共鉴定出213个重叠的DEGs,其中109个上调,104个下调。GO分析显示,DEGs主要参与有丝分裂和细胞分裂。KEGG通路分析表明,大多数DEGs在细胞周期通路中显著富集。GSEA显示,DEGs主要富集在卵母细胞减数分裂、细胞周期和DNA复制通路中。鉴定出一个关键模块,并筛选出10个枢纽基因(、、、、、、、、和)。表达和生存分析表明,这10个枢纽基因在SS患者中上调,可导致生存率显著降低。我们的研究鉴定了一系列参与SS进展的与转移相关的生物标志物,并可能为SS转移提供新的治疗靶点。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ac53/7518102/bc15cbd227a0/fgene-11-530892-g001.jpg

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